CNN-N-Gram for Handwriting Word Recognition

Arik Poznanski, Lior Wolf; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2305-2314

Abstract


Given an image of a handwritten word, a CNN is employed to estimate its n-gram frequency profile, which is the set of n-grams contained in the word. Frequencies for unigrams, bigrams and trigrams are estimated for the entire word and for parts of it. Canonical Correlation Analysis is then used to match the estimated profile to the true profiles of all words in a large dictionary. The CNN that is used employs several novelties such as the use of multiple fully connected branches. Applied to all commonly used handwriting recognition benchmarks, our method outperforms, by a very large margin, all existing methods.

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[bibtex]
@InProceedings{Poznanski_2016_CVPR,
author = {Poznanski, Arik and Wolf, Lior},
title = {CNN-N-Gram for Handwriting Word Recognition},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}